Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2025
Forfattere
Alexander N. Hristov André Bannink M Battelli Alejandro Belanche M.C. Cajarville Sanz G Fernandez-Turren F Garcia Arjan Jonker D.A. Kenny Vibeke Lind S.J. Meale D Meo Zilio Camila Muñoz David Pacheco Nico Peiren Mohammad Ramin L Rapetti Angela Schwarm Sokratis Stergiadis Katerina Theodoridou E.M. Ungerfeld S van Gastelen D.R. Yanez-Ruiz S.M. Waters Peter LundSammendrag
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Sammendrag
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Sammendrag
Urban green structures (UGS) play important roles in enhancing urban ecosystems by providing benefits such as mitigating the urban heat island effect, improving air quality, supporting biodiversity, and aiding in stormwater management. Accurately mapping UGS is important for sustainable urban planning and management. Traditional methods of mapping such as manual mapping, aerial photography interpretation and pixel-based classification have limitations in terms of coverage, accuracy, and efficiency. Object-based image analysis (OBIA) has gained prominence due to its ability to incorporate both spectral and spatial information making it particularly effective for classification of high-resolution satellite data. This paper reviews the application of OBIA on satellite images for UGS mapping, focusing on various data sources, popular segmentation methods, and classification techniques, highlighting their respective advantages and limitations. Key segmentation methodologies discussed include multi-resolution segmentation and watershed segmentation. For classification, the review covers machine learning techniques such as random forests, support vector machines, and convolutional neural networks, among others. Several case studies highlight the successful implementation of OBIA in diverse urban environments by demonstrating improvements in classification accuracy and detail. The review also addresses the challenges associated with OBIA, such as dealing with heterogenous urban landscapes, data sources and with OBIA methods itself. Future directions for UGS mapping include the integration of deep learning algorithms, advancements in satellite data technologies, and the development of standardized classification frameworks. By providing a detailed analysis of the current state-of-the-art in object-based UGS mapping, this review aims to guide future research and practical applications in UGS management.
Sammendrag
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Forfattere
Anders Lorentzen Kolstad Joachim Paul Töpper Marianne Evju Line Johansen Magni Olsen Kyrkjeeide Anders Lyngstad Jan Ketil Rød Lise Tingstad Liv Guri VelleSammendrag
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Forfattere
Mostafa Hoseini Helle Ross Gobakken Stephan Hoffmann Csongor Horvath Johannes Rahlf Jan Bjerketvedt Stefano Puliti Rasmus AstrupSammendrag
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Sammendrag
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Sammendrag
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Forfattere
Jian Liu Faruk Djodjic Barbro Ulén Helena Aronsson Marianne Bechmann Lars Bergström Tore Krogstad Katarina KyllmarSammendrag
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Sammendrag
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